Tootfinder

@… It talks about how it is wrong, but not in the most important way.
It was written in 2016, so perhaps that’s forgiveable.

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Improved Distributed Algorithms for Random Colorings
Charlie Carlson, Daniel Frishberg, Eric Vigoda

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Wei-You Liao, Yuxuan Du, Xinbiao Wang, Tian-Ci Tian, Yong Luo, Bo Du, Dacheng Tao, He-Liang Huang
https://arxiv.org/abs/2507.17470

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum processors means they will remain rare for the foreseeable future, limiting their widespread application. To address this bottleneck, we introduce the concept of predictive surrogates, which are classical learning models designed to emulate the mean-value behavior of a given quantum processor with provably computational eff…

I will never forgive Markdown for supporting <del> but not <ins>.

FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Yiming Zhong, Yumeng Liu, Chuyang Xiao, Zemin Yang, Youzhuo Wang, Yufei Zhu, Ye Shi, Yujing Sun, Xinge Zhu, Yuexin Ma
https://arxiv.org/abs/2506.01583

FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing methods remain unsatisfactory due to inherent limitations in the essential action representation and the basic network architectures. We observe that representing actions in the frequency domain captures the structured nature of motion more effectively: low-frequency components reflect global movement patterns, while high-…

Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
Sergio Mazzola, Gabriele Ara, Thomas Benz, Bj\"orn Forsberg, Tommaso Cucinotta, Luca Benini
https://arxiv.org/abs/2506.23672

Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel power modeling methodology with state-of-the-art accuracy, low overhead, and h…